A review of hand gesture and sign language recognition techniques

• Published in: International journal of machine learning and cybernetics Vol. 10; no. 1; pp. 131 - 153
• Main Authors: Cheok, Ming Jin, Omar, Zaid, Jaward, Mohamed Hisham
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2019; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Camcorders; Cameras; Communication; Complex Systems; Computational Intelligence; Control; Data acquisition; Deafness; Engineering; Feature extraction; Gesture recognition; Literature reviews; Mechatronics; Original Article; Pattern Recognition; Performance evaluation; Robotics; Sensors; Sign language; State-of-the-art reviews; Systems Biology; Virtual reality; Voice recognition; Webcams; Computer vision; Sign language; Image processing; Gesture recognition; Machine learning
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-017-0705-5

Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research. The techniques reviewed are suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification, where the various algorithms at each stage are elaborated and their merits compared. Further, we also discuss the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition. Overall, it is hoped that the study may provide readers with a comprehensive introduction into the field of automated gesture and sign language recognition, and further facilitate future research efforts in this area.